Background Noise Interference Research Articles

Group-Sparsity Learning Approach for Bearing Fault Diagnosis

Fault impulse extraction under strong background noise and/or multiple interferences is a challenging task for bearing fault diagnosis. Sparse representation has been widely applied to extract fault impulses and can achieve state-of-the-art performance. However, most of the current methods rely on carefully tuning several hyperparameters and suffer from possible algorithmic degradation due to the approximate regularization and/or heuristic sparsity model. To overcome these drawbacks, in this article, we present a sparse Bayesian learning (SBL) framework for bearing fault diagnosis, and then propose two group-sparsity learning algorithms to extract fault impulses, where the first one exploits the group-sparsity of fault impulses only, whereas the second one utilizes additional periodicity behavior of fault impulses. Due to the inherent learning capability of the SBL framework, the proposed algorithms can tune hyperparameters automatically and do not require any prior knowledge. Another advantage is that our solutions are maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$posteriori$</tex-math></inline-formula> estimators in the sense of Bayesian optimality, which can yield higher accuracy. Results on both simulated and real datasets demonstrate the superiority of the developed algorithms.

Feature Enhanced Fully Convolutional Network for Ship Detection

Focusing on the problem of high false detection and missed detection in the existing ship detection methods, which is affected by the background noise on sea surface and the various scales of different types of ships, a novel feature enhanced fully convolutional network is proposed for ship detection. First, a multi-scale feature enhancement mechanism is introduced to improve the feature descriptive power of the model for ships. In each single-scale feature extraction stage, the statistical information is calculated and utilized to suppress the interference of background noise, such as wave and clutter, on the feature description of ships. Meanwhile, in the multi-scale feature fusion stage, two steps feature adaptive fusion strategy is utilized to improve the network awareness of multi-scale ships. Furthermore, an improved object detection head network is devised under the regression analysis framework, which optimizes the calculation of centerness through an attention enhancement mechanism in regression branch to better suppress the low-quality bounding boxes, and improve ship detection performance while ensuring detection efficiency. The mean average precision of the proposed method reaches 91.6% on the large-scale maritime surveillance dataset SeaShips, and the detection speed is 9 frames per second. Experimental results demonstrate the proposed method has good detection accuracy and robustness.

Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis

Rolling bearing fault signals are non-smooth, non-linear, and susceptible to background noise interference. A feature layer fusion model combining multi-sensor signals and parallel attention convolutional neural networks is proposed and applied to the fault diagnosis work of rolling bearings. First, a multi-channel parallel convolutional neural network model is constructed according to the number of sensors, and the multi-sensor signals are fed to each parallel channel separately. Second, due to the different strengths of shock features within each channel and signal, the attention mechanism is introduced into each parallel channel, the fault features with strong shock characteristics are extracted, and the feature extraction capability for different sensor signals is improved. Finally, the extracted feature information is fused in the concatenate layer, the fused features are input to the fully connected layer, and the diagnosis results are output through the Softmax layer. The experimental results show that the model can effectively fuse multi-sensor signal features, and its recognition accuracy is greatly improved over that of the single sensor, providing a feasible method for bearing fault diagnosis.

FRCNet: Feature Refining and Context-Guided Network for Efficient Polyp Segmentation.

Colorectal cancer, also known as rectal cancer, is one of the most common forms of cancer, and it can be completely cured with early diagnosis. The most effective and objective method of screening and diagnosis is colonoscopy. Polyp segmentation plays a crucial role in the diagnosis and treatment of diseases related to the digestive system, providing doctors with detailed auxiliary boundary information during clinical analysis. To this end, we propose a novel light-weight feature refining and context-guided network (FRCNet) for real-time polyp segmentation. In this method, we first employed the enhanced context-calibrated module to extract the most discriminative features by developing long-range spatial dependence through a context-calibrated operation. This operation is helpful to alleviate the interference of background noise and effectively distinguish the target polyps from the background. Furthermore, we designed the progressive context-aware fusion module to dynamically capture multi-scale polyps by collecting multi-range context information. Finally, the multi-scale pyramid aggregation module was used to learn more representative features, and these features were fused to refine the segmented results. Extensive experiments on the Kvasir, ClinicDB, ColonDB, ETIS, and Endoscene datasets demonstrated the effectiveness of the proposed model. Specifically, FRCNet achieves an mIoU of 84.9% and mDice score of 91.5% on the Kvasir dataset with a model size of only 0.78 M parameters, outperforming state-of-the-art methods. Models and codes are available at the footnote. 1

Method improving low Signal-to-noise ratio of velocity test signals for Laser-induced shock waves

When laser-induced shock (LIS) wave characteristics testing is carried out using laser interference methods, the electrical noise, vibration noise, jitter or offset of the diffuse reflective surface during high-speed motion and other factors can lead to superposition or offset of the noise signal frequency with the low-frequency phase of the useful signal. As a result, problems such as phase shift of the useful signal and difficulty in extracting the initial position of the wavelet ridge may appear, and the initial position cannot be zeroed. By introducing a measurement factor through multi-resolution singular value decomposition and adaptively determining the optimal number of decomposition layers, zero-phase offset filtering is achieved and the signal-to-noise ratio (S/N) of the data is improved. According to the waveform decay characteristics of laser-induced shock wave velocity signals, the Shannon wavelet entropy method is proposed to accurately optimise the Morlet wavelet basis parameters to enhance the aggregation of wavelet scale spectrum time–frequency distribution. Simulation and test results show that: the proposed method overcomes the background noise interference and enables the initial velocity to be zeroed; the relative singular value points of the pulse are accurately symmetrically distributed and no phase shift occurs; the denoising performance is better than that of the wavelet method, the root mean square error is only 0.046, and the repeatability is strong; the error of the elastic wave velocity of the laser-induced shock wave at different power densities is within 3.2%; the relative velocity resolution remains constant at low/high frequencies, effectively avoiding false peaks and misclassification of the velocity profile.

Deep learning for intelligent identification of concrete wind-erosion damage

In the desert region of northwest China, the frequency of wind-sand disasters is high. All types of concrete buildings built in this area face severe wind erosion due to high wind speed, resulting in varying degrees of wind-erosion damage to concrete. To accomplish intelligent identification of concrete wind-erosion damage, a concrete wind erosion experiment was conducted in the laboratory, and a concrete wind-erosion damage dataset was generated under the interference of water stains, scratches, shooting distance, and background noise. This paper combined with transformer theory to improve YOLO-v4 and proposed an object detection algorithm called MHSA-YOLOv4 suitable for wind-erosion damage of concrete. The results demonstrate that MHSA-YOLOv4 exhibits improved object detection performance than YOLO-v3, improved YOLO-v3, and YOLO-v4. On the test set, ACC, Precision, Recall, and mAP of MHSA-YOLOv4 are 91.30%, 91.52%, 92.31%, and 0.89, respectively. MHSA-YOLOv4 can accurately identify wind-erosion damage of concrete images under different test conditions, which reflects strong robustness. The applicability of computer vision technology to the intelligent identification of wind-erosion damage on concrete has been verified.

Aquila-Eagle-Based Deep Convolutional Neural Network for Speech Recognition Using EEG Signals

The conventional BCI system experiences several issues such as background noise interference, lower precision rate and high cost. Hence, a novel speech recognition model which is based on the optimized Deep-CNN is proposed in this research article so as to restrain the issues related to the conventional speech recognition method. The significance of the research relies on the proposed method algorithm known as Aquila-eagle optimization, which effectively tunes the parameters of Deep-CNN. The most significant features are extracted in the feature selection process, which enhance the precision of the speech recognition model. Further unwanted noises in the EEG signals are constructively removed in the pre-processing stage to boost the accuracy of the Deep-CNN classifier.From the experimental outcomes it is demonstrated that the proposed Aquila-eagle-based DeepCNN outperformed other state-of-the-art techniques in terms of accuracy, precision, and recall with the values of 93.11%, 90.89%, and 93.11%, respectively.

Abstract 1228: Spatial transcriptomics: Data processing revisited to address noise interference

Abstract Background: Spatially resolved transcriptomics is a novel and already highly recognized method that allows RNA sequencing results to be annotated with local tissue phenotypes. The NanoString GeoMx Digital Spatial Profiling (DSP) Platform allows users to collect RNA expression data from manually selected Regions of Interest (ROIs) on FFPE tissue sections. Here, we extensively evaluated data from the DSP platform with its associated pipeline and identify significant background noise interference issues which compromise data interpretation. Alternative and more suitable workflows are presented for correct data analysis. Methods: In this study, 12 paired tumor samples were collected from six glioma patients who underwent two separate resections. For all patients, the first resection was a low grade astrocytoma (WHO grade II or III) and the second resection was a high grade astrocytoma (WHO grade IV). The DSP platform was used to collect expression data of 1,800 genes from 72 ROIs (i.e. 6 per sample). Biological replicates were made of eight tumors from four patients. Gene expression data was normalized with both standard NanoString methods and several alternative methods (e.g. DeSeq2, gamma fit correction and quantile normalization). Weighted Gene Co-expression Network analysis (WGCNA) was used for biological validation. In addition to our own study, six publicly available NanoString DSP datasets were evaluated. Results: Data distributions of all glioma samples, when exposed to standard data processing, were burdened with significant background noise interference. Notably, differences in noise interference were largest between biologically distinct tumor subgroups (i.e. between first and second glioma resections), which was confirmed in replicate experiments. The noise interference patterns were also present in all six publicly available NanoString DSP datasets which will invariably lead to incorrect interpretation of the underlying biology. To correct for noise interference, we tested several normalization methods. The relatively crude quantile normalization method provided the least biased result and showed the highest concordance with bulk RNA sequencing data. To evaluate the biological validity of our alternative approach, we used T cell counts from our tissue regions as an independent parameter, that were quantified using immune fluorescence. Unsupervised WGCNA identified gene clusters enriched for lymphocyte genes that highly correlated with T cell quantities in ROIs, confirming that alternative normalization can extract a biological signal from the DSP platform. Conclusion: The DSP Platform platform suffers from significant noise interference when using standard analysis tools that obscure its results. Here, we revised the workflow and provide an alternative normalization that adequately addresses noise interference and enables correct interpretation of gene expression data. Citation Format: Levi van Hijfte, Marjolein Geurts, Wies R. Vallentgoed, Paul H. Eilers, Peter A. Sillevis Smitt, Reno Debets, Pim J. French. Spatial transcriptomics: Data processing revisited to address noise interference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1228.

Acoustic hologram of the metasurface with phased arrays via optimality criteria

Holography is a technique to generate arbitrarily complex acoustic fields, fundamental to applications such as acoustic imaging, particle manipulation, and energy deposition. In this paper, we develop a new approach for designing acoustic holograms with high-resolution sound images against background noise interference. The metasurface-based holograms are composed of phased arrays, such as helical units, to adjust the propagating sound phase from 0 to 2π flexibly. The problem aims to seek the optimal phase distribution at the output of the metasurface, so that sounds focus a prescribed image at the projection plane. The projection plane is divided into the target and background pixels according to the desired resolution of images. The optimization problem is established to maximize the sound intensity of the target pixels and minimize that of the background pixels simultaneously. The optimality criterion is adopted to iteratively update the phase distribution in arrays based on the derived sensitivity. Finally, the effectiveness of the proposed approach is validated numerically and experimentally.

An Adaptive Sample Assignment Strategy Based on Feature Enhancement for Ship Detection in SAR Images

Recently, ship detection in synthetic aperture radar (SAR) images has received extensive attention. Most of the current ship detectors preset dense anchor boxes to achieve spatial alignment with ground-truth (GT) objects. Then, the detector defines the positive and negative samples based on the intersection-over-unit (IoU) between the anchors and GT objects. However, this label assignment strategy confuses the learning process of the model to a certain extent and results in suboptimal classification and regression results. In this paper, an adaptive sample assignment (ASA) strategy is proposed to select high-quality positive samples according to the spatial alignment and the knowledge learned from the regression and classification branches. Using our model, the selection of positive and negative samples is more explicit, which achieves better detection performance. A regression guided loss is proposed to further lead the detector to select well-classified and well-regressed anchors as high-quality positive samples by introducing the regression performance as a soft label in the calculation of the classification loss. In order to alleviate false alarms, a feature aggregation enhancement pyramid network (FAEPN) is proposed to enhance multi-scale feature representations and suppress the interference of background noise. Extensive experiments using the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) demonstrate the superiority of our proposed approach.

Secure and noise-resistant underwater wireless optical communication based on spectrum spread and encrypted OFDM modulation.

In addition to requirements on increasing transmission distance and bitrate, the study of underwater wireless optical communication (UWOC) is also facing limitations and challenges, such as interference induced by background noise, demand of higher receiver sensitivity, and communication security issues. In this paper, we experimentally demonstrate a physical layer secure and noise-resistant UWOC system based on spectrum spread and encrypted orthogonal frequency division multiplexing (SSE-OFDM) modulation, transmission through a 14.2 m sediment circulating water tank. Firstly, experimental results show that the required optical power ratio of signal and noise light (OPR) for QPSK signal under BER threshold of 3.8×10-3 is around -5.77 dB for a spectrum spread factor (N) of 100, with a signal-to-noise ratio (SNR) improvement of 19.06 dB. Secondly, without the background noise interference, the receiver sensitivity is also improved from -50 dBm to -62.4 dBm by using the SSE-OFDM modulation, achieving a maximum attenuation length (AL) of 19.67. Thirdly, physical layer security of UWOC can also be realized, which suppresses the SNR of eavesdropper to -3.72 dB while improving SNR of the authorized receiver to 17.56 dB under the condition of no leakage of keys. Additionally, analytical expressions for SSE-OFDM based UWOC performance are also derived, which agree well with the experimental results. Based on the analytical expressions, the maximum secrecy capacity Cs for SSE-OFDM based UWOC system under eavesdropping can be obtained by optimizing the intentionally inserted artificial noise power ratio and the spectrum spread factor N.

An Improved Cervical Cell Segmentation Method Based on Deep Convolutional Network

The cervical cytology smear test is an effective method for cervical cancer early screening, and segmentation accuracy is essential for computer-aided diagnosis. In this study, an improved cervical nucleus and cytoplasm segmentation method based on a deep convolutional network was proposed. This method consisted of a cellular region proposal and pixel-level segmentation network (CRP-PSN). Data were obtained from the 2014 International Symposium on Biomedical Imaging cervical cell segmentation competition open dataset. In CRP networks, online hard example mining and soft-nonmaximum suppression algorithms were incorporated to solve problems, including background noise, impurities, and other interferences in smear images. For PSN networks, a generative adversarial network-generated adversarial network algorithm and least-squares loss function were used to generate cell region segmentation, thereby improving the cytoplasm segmentation results. Finally, the improved CRP-PSN model was analyzed and compared with other typical cervical cell segmentation methods. The experimental results showed that the proposed model could effectively improve the segmentation accuracy of cytoplasm and nuclei in cervical cytology smear images by 92% and 98.6%, respectively. These findings provided strong support for the application of this method for automated interpretation of cervical cytology smear images and improved diagnostic reliability.

Cross‐domain person re‐identification based on background suppression and identity consistency

Recent advanced methods address the cross-domain person re-identification (Re-ID) problem primarily by the pseudo label estimation. However, person images with similar backgrounds are usually incorrectly clustered due to the interference of background noise. Furthermore, it is the lack of filtering of incorrect pseudo labels that results in the poor quality of the final generated pseudo labels. Here, a progressive learning approach based on background suppression and identity consistency for cross-domain person Re-ID (BSIC-reID) is proposed. In the background suppression module, background mask attention and reverse attention are combined to effectively extract pedestrian features and suppress background noise, highlighting the foreground person information for person Re-ID. In addition, the BSIC-reID model is used to extract multi-scale person features and generate different perspective pseudo labels for the target domain images. The incorrect pseudo labels are filtered by comparing the potential similarity of multi-scale person features so that the higher quality pseudo labels for the target domain images could be generated. This method is performed on the Market-1501, DukeMTMC-reID, and MSMT17, evaluated by the Cumulated Matching Characteristics (CMC) and mean Average Precision (mAP). The experimental results demonstrate that this method also achieves state-of-the-art performance.

Application of FIFD-OMGD-1.5D Teager Energy to Extract Microfault Features of Rolling Bearing

Deconvolution-related methods are the mainstream choice when it comes to enhancing the pulse impact of bearing fault and reducing noise interference. Kurtogram algorithm is used to optimize the minimum generalized Lp/Lq deconvolution to improve the nonconvexity of other optimization criteria. However, it has low computational efficiency and poor diagnostic accuracy under strong background noise. The paper proposes an optimized method using protrugram algorithm that combines fast iterative filter decomposition (FIFD) with minimum generalized Lp/Lq deconvolution (OMGD) for the 1.5-dimension Teager energy spectrum demodulation. Here is the specific process of the application: Fast iterative filtering (FIF) was used to reduce noise interference before using the maximum kurtosis to obtain the center frequency and frequency band and optimize the filter design, which was for the MGD initialization operation to prevent the result from falling into the local optimal solution and check the interference of impulse noise to a certain extent. The 1.5-dimension Teager energy spectrum was then used for demodulation analysis to extract small fault features of rolling bearings. The verification of simulation signals and actual data showed that this method was better in terms of extraction effect and efficiency than the use of fast kurtogram algorithm to optimize minimum generalized Lp/Lq deconvolution when it comes to extracting microfault features with high interference of background noise.

Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings

The fault vibration signal of rolling bearing is typical non-stationary and non-linear signals. It usually exhibits the characteristics of amplitude modulation and frequency modulation (AM-FM) due to the interaction between the various components within the bearing and modulation of the rotor speed. In addition, the incipient weak fault feature information is inevitably masked by the strong background noise and other vibration interference because of the influence of the working environment of mechanical equipment. Therefore, new high efficiency and effective processing methods are indispensable for feature extraction of bearing fault diagnosis signals. Direct fast iterative filtering (dFIF) is a new kind of adaptive mode decomposition method proposed by Antonio Cicone based on iterative filtering (IF) and fast iterative filtering (FIF). dFIF is used to quickly decompose multi-component signals into a set of intrinsic mode functions (IMFs) by means of fast Fourier transform (FFT). In order to study the decomposition performance and examine the feature extraction ability of the dFIF method, a group of harmonic signals with simple linear superposition, multi-components AM-FM signals with different data length and different signal-to-noise ratio (SNR) are simulated and analyzed. For comparison, other mode decomposition methods were also applied to the simulated signals, such as adaptive local iterative filtering (ALIF), local mean decomposition (LMD), and empirical mode decomposition (EMD). The results show that dFIF decomposition methods have faster decomposition speed and better accuracy than other methods. Furthermore, the results also demonstrate excellent performance in AM-FM feature extraction and anti-mode mixing. On the other hand, the effective weighted sparseness kurtosis (EWSK) index integrates the periodicity and intensity of impact in each mode, which can effectively identify the effective mode. Therefore, considering the above merits, EWSK is integrated on the dFIF to effectively accomplish the fault diagnosis for rolling bearing under strong background noise. Firstly, the raw vibration signals of rolling bearing fault are decomposed into a set of IMFs by using dFIF. The EWSK indicator is then utilized to select IMFs with rich bearing fault impulse information for reconstruction. Finally, the Hilbert envelope demodulation analysis is used to analyze the reconstructed signal, extract the bearing fault feature and then judge the fault type. This proposed method is applied to analyze the simulated and field measured vibration signals of rolling bearing faults. Simultaneously, the other above-mentioned adaptive TFA methods are also adopted to analyze the signals. The results compared show that the proposed method has excellent performance in the aspects of decomposition speed and accuracy under the influence of strong background noise. It demonstrates the effectiveness and applicability of the proposed method for signal processing and fault pattern recognition of rolling bearings.

Improved Thyreostatic Drug Detection in Animal Tissues Using Liquid Chromatography-High-Field Asymmetric Waveform Ion Mobility Spectrometry-Mass Spectrometry.

Thyreostatic drugs (thyreostats) interfere with thyroid function and have been used illegally in animals slaughtered for food. Thyreostat use leads to poorer quality meat, and the drug residues can cause adverse effects in humans. These drugs, with the exception of thiouracil, do not occur naturally and require sensitive methodologies for their detection in animal tissues. Because thyreostats are low-molecular-weight polar analytes, liquid chromatography-mass spectrometry (LC-MS) is typically used for detection and, in particular, triple quadrupole mass spectrometry with selective reaction monitoring (i.e., LC-SRM). However, LC-SRM thyreostat methods suffer from chemical background noise and endogenous interferences arising from the complex tissue matrix. An improved high-field asymmetric waveform ion mobility spectrometry interface (FAIMS Pro), which separates ions based on differential ion mobility, was combined with LC-SRM to minimize these interferences. Using the same samples and conditions, LC-FAIMS-SRM showed improvements in the signal-to-noise ratio (S/N) of up to 50 times compared with our validated LC-SRM method. In addition, wider linear ranges, including substantial improvements in the lower limit of quantification (approximately an order of magnitude for tapazole and methylthiouracil), were observed with LC-FAIMS-SRM.

Sonar Image Target Detection Based on Adaptive Global Feature Enhancement Network

Automatic underwater target detection plays a vital role in sonar image processing and analysis, and its core task is to discriminate target categories and achieve precise positioning. However, the sonar image is interfered by the seafloor reverberation noise and complex background, which brings more significant challenges to the accurate detection of sonar target. To achieve accurate detection of different categories targets in sonar image, we proposed an adaptive global feature enhancement network (AGFE-Net), which uses multi-scale convolution and attention mechanisms with global receptive field to obtain sonar image multi-scale semantic feature and enhance the correlation between features. Specifically, we use the multi-scale receptive field feature extraction block (MSFF-Block) and the self-attention mechanism block (SAM-Block) to enhance model feature extraction ability; the bidirectional feature pyramid network (Bi-FPN) and the global pyramid pooling block (GPP-Block) are used to obtain the deep semantic feature and suppress background noise interference; the adaptive feature fusion block (AFF-Block) is used to effectively fuse features of different scales. Experimental results on the presented sonar target detection dataset WH-Dataset and QD-Dataset validate the advantage of AGFE-Net over other state-of-the-art target detection methods.

Bidirectional Denoising Autoencoders-Based Robust Representation Learning for Underwater Acoustic Target Signal Denoising

The marine environmental noise formed by wind noise, rain noise, biological noise, sea surface waves, seismic disturbances, and so on is a kind of interference background field in underwater acoustic channels, which brings adverse effects to underwater acoustic target recognition. To improve the recognition accuracy of underwater targets under background noise interference, a bidirectional denoising autoencoder (BDAE) is proposed in this paper for underwater acoustic target signal denoising robust representation learning. The proposed bidirectional denoising autoencoder is an extension of the regular denoising autoencoder, which uses the original underwater acoustic target signals and their corresponding denoised signals to learn robust representations. We then measure the usefulness of the learned representations using a support vector machine classifier. Our proposed approach is verified on the ShipsEar database. Experimental results indicate that the proposed bidirectional denoising autoencoder can effectively learn the robust representations of underwater acoustic target signal denoising and is superior to the traditional methods.

Background Noise Resistant Underwater Wireless Optical Communication Using Faraday Atomic Line Laser and Filter

High sensitive underwater wireless optical communication (UWOC) in shallow water or FSO-UWOC convergent applications suffers severely from solar noise and other background noise interference. This paper presents experimental demonstrations of broadband background noise resistant air-water and underwater wireless optical communication by using a pair of spectrum matched 852 nm Cs atomic Faraday line laser and filter, with a narrow passband of only 1 GHz. Experimental results show that by using the Faraday atomic line laser and filter, the Q factor can be improved by 3.37 dB compared to using the interference filter, and 14.67 dB compared to system without using noise resistant filter. A numerical model for UWOC system performance affected by broadband background noise with various filtering conditions is presented, where both the numerical and experimental results match each other quite well. Based on the experimental results substantiated model, performance of UWOC under solar noise interference is numerically studied. The numerical results show that under strong solar noise interference, the maximum depth of the receiver is extended from 0.77 m to 3.79 m for FSO-UWOC system and from 0.87 m to 3.54 m for UWOC system. It proves that FADOF can provide considerable performance improvement for both UWOC and FSO-UWOC system with broadband background noise interferences.

Infrared Small Target Detection Based on the Improved Density Peak Global Search and Human Visual Local Contrast Mechanism

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. However, IR small targets are very difficult to detect because of their weak brightness, small size, and lack of shape, structure, texture, and other information elements. In order to simultaneously satisfy the robustness and timeliness of target detection, inspired by density peak clustering and the human visual system, an idea combining an improved density peak global search and local contrast calculation is proposed. First, the positions of candidate targets are determined in the preprocessed image using the improved density peak global search method (IDPGSM). Second, the saliency map is obtained using the double-weights enhanced local contrast method (DWELCM) for the candidate target neighborhood. Finally, adaptive threshold segmentation is used to detect IR small targets. Through comprehensive analysis of five evaluation indicators, the experimental results on seven real sequences and three hundred IR images of different scenes that the proposed method has better detection performance compared with six baseline methods. It can quickly and accurately determine the small target position in the case of severe background clutter and noise interference.